Patent Application
20240375786
This disclosure relates generally to aerial refueling, and more particularly to controlling aerial refueling operation.
Automation of aerial refueling provides safety benefits for a tanker aircraft and a receiver aircraft. However, accurately and efficiently conducting a refueling operation using cameras can be difficult when practicing current automated techniques.
The subject matter of the present application has been developed in response to the present state of the art, and in particular, in response to the shortcomings of conventional aerial refueling techniques, that have not yet been fully solved by currently available techniques. Accordingly, the subject matter of the present application has been developed to provide systems and methods for providing aerial refueling techniques that overcome at least some of the above-discussed shortcomings of prior art techniques.
The following is a non-exhaustive list of examples, which may or may not be claimed, of the subject matter, disclosed herein.
In one example, a method includes receiving a 2D image from a camera, determining two-dimensional (2D) keypoints of a target object located within the 2D image based on a predefined model of the target object, estimating a 6 degrees-of-freedom (6DOF) pose based on the 2D keypoints and a three-dimensional (3D) model of the target object, generating an uncertainty value of the 6DOF pose, and outputting the uncertainty value of the 6DOF pose.
In another example, a tanker aircraft includes a refueling boom, a camera configured to generate a 2D image of an in-flight refueling operation between a receiver aircraft and the tanker aircraft, a processor, and non-transitory computer readable storage media storing code. The code is executable by the processor to perform operations including determining 2D keypoints of a target object located within the 2D image based on a predefined model of the target object, estimating a 6DOF pose based on the 2D keypoints and a 3D model of the target object, generating an uncertainty value of the 6DOF pose, and outputting the uncertainty value of the 6DOF pose.
In still another example, a system includes a camera configured to generate a 2D image of an in-flight refueling operation between a receiver aircraft and the tanker aircraft, a processor, and non-transitory computer readable storage media storing code. The code is executable by the processor to perform operations including determining 2D keypoints of a target object located within the 2D image based on a predefined model of the target object, estimating a 6DOF pose based on the 2D keypoints and a 3D model of the target object, generating an uncertainty value of the 6DOF pose, and outputting the uncertainty value of the 6DOF pose.
The described features, structures, advantages, and/or characteristics of the subject matter of the present disclosure may be combined in any suitable manner in one or more examples and/or implementations. In the following description, numerous specific details are provided to impart a thorough understanding of examples of the subject matter of the present disclosure. One skilled in the relevant art will recognize that the subject matter of the present disclosure may be practiced without one or more of the specific features, details, components, materials, and/or methods of a particular example or implementation. In other instances, additional features and advantages may be recognized in certain examples and/or implementations that may not be present in all examples or implementations. Further, in some instances, well-known structures, materials, or operations are not shown or described in detail to avoid obscuring aspects of the subject matter of the present disclosure. The features and advantages of the subject matter of the present disclosure will become more fully apparent from the following description and appended claims or may be learned by the practice of the subject matter as set forth hereinafter.
In order that the advantages of the subject matter may be more readily understood, a more particular description of the subject matter briefly described above will be rendered by reference to specific examples that are illustrated in the appended drawings. Understanding that these drawings depict only typical examples of the subject matter, they are not therefore to be considered to be limiting of its scope. The subject matter will be described and explained with additional specificity and detail through the use of the drawings, in which:
Reference throughout this specification to “one example,” “an example,” or similar language means that a particular feature, structure, or characteristic described in connection with the example is included in at least one example of the present disclosure. Appearances of the phrases “in one example,” “in an example,” and similar language throughout this specification may, but do not necessarily, all refer to the same example. Similarly, the use of the term “implementation” means an implementation having a particular feature, structure, or characteristic described in connection with one or more examples of the present disclosure, however, absent an express correlation to indicate otherwise, an implementation may be associated with one or more examples.
Disclosed herein is a refueling system 102 located on a tanker aircraft 100 that provides a determination of whether a two-dimensional (2D) to three-dimensional (3D) pose estimation system is correct. This determination can be supplied to an aerial refueling system for controlling output to receiver aircraft pilots, boom operators, and/or automated aerial refueling components during aerial refueling operations. As shown in
In various embodiments, referring to
In various embodiments, the boom operator interface 110 includes a user interface device 130 and a monitor 132. Images presented on the monitor 132 are based on information provided by the processor 104. The director light system 108 includes a switching unit 140 and an array of lights 142 (i.e., pilot director lights). The switching unit 140 controls activation of the array of lights 142 based on information provided by the processor 104. The automated refueling system 112 controls operation of the refueling boom 204 and/or the tanker aircraft 100 based on information provided by the processor 104.
In various embodiments, the array of lights 142 is located on the lower forward fuselage of the tanker aircraft 100. The array of lights 142 is positioned to be clearly viewable by the pilot of the receiver aircraft 202. The array of lights 142 include various lights for providing directional information to the pilot of the receiver aircraft 202. The array of lights 142 may include an approach light bar, an elevation light bar, a fore/aft position light bar, four longitudinal reflectors, two lateral reflectors, or other lights.
Referring to
It can be appreciated that refueling or close quarter operations may occur between other vehicles not just the aircraft 100, 202 depicted. The refueling or close quarter operations may occur during adverse weather conditions. The vehicles may be any vehicles that move relative to each other (in water, on land, in air, or in space). The vehicles may also be manned or unmanned. Given by way of non-limiting example, in various embodiments, the vehicles may be a motor vehicle driven by wheels and/or tracks, such as, without limitation, an automobile, a truck, a cargo van, and the like. Given by way of further non-limiting examples, in various embodiments, the vehicles may include a marine vessel such as, without limitation, a boat, a ship, a submarine, a submersible, an autonomous underwater vehicle (AUV), and the like. Given by way of further non-limiting examples, in various embodiments, the vehicles may include other manned or unmanned aircraft such as, without limitation, a fixed wing aircraft, a rotary wing aircraft, and a lighter-than-air (LTA) craft.
In various embodiments, non-transitory computer readable instructions (i.e., code) stored in the memory 114 (i.e., storage media) cause the processor 104 to use raw image data from a single sensor (i.e., the camera 120) and make the raw data scalable and cost effective to integrate into existing systems. In particular, the processor 104 predicts keypoints 310 (see, e.g.,
In various embodiments, non-transitory computer readable instructions (i.e., code) stored in the memory 114 (i.e., storage media) cause the processor 104 to predict keypoints 310 (see, e.g.,
In various embodiments, the processor 104 trains a convolutional neural network (CNN) to identify features/keypoints on the 3D model (computer aided design (CAD) model) from a 2D image. The CNN is based on residual network (ResNet) architecture. The CNN removes final pooling and fully connected layers of the architecture and replaces them with a series of deconvolutional and/or upsampling layers to return an output image matching the height and width of the input image with the number of keypoints matching a number channels. Each of the channels is considered to be a heatmap of where the keypoint is located in 2D image space. From the heatmap, the pixel at the center of the distribution represented by the heatmap is chosen to be the position of the keypoint (i.e., the 2D keypoint predictions).
In various embodiments, referring to
Each of the predicted 2D keypoints 310, 320 are compared with the corresponding 3D model keypoints using the PnP pose algorithm to produce a 6DOF pose estimate of the position of the receiver aircraft 202 or the refueling boom 204. Then, the processor 104 analyzes the 6DOF pose estimate for potential error. The processor 104 produces a confidence or uncertainty value associated with the 6DOF pose estimate. First, the processor 104 determines a reprojection error. The reprojection error includes a reprojection error for the i-th keypoint estimate. The reprojection error is calculated as the 2D distance between the i-th estimated 2D keypoint and the 2D projection of the i-th 3D model keypoint, using the solved 6DOF pose.
Reprojection Error for ith keypoint=−P (ki, R, t)
The reprojection error is used to sample a distribution of new sets of keypoints and calculate poses for sampled keypoint sets in order to form a distribution of 6DOF pose results. The processor 104 samples M new sets of keypoints. In order to sample the i-th keypoint in the j-th new set of keypoints, the processor 104 samples noise from a 2D normal distribution with 0 mean and identity covariance. Next, the processor 104 multiplies (i.e., scales) the sampled noise by the absolute value of the reprojection error and a scaling factor which is used to tune the result. The processor 104 then adds the scaled noise to the 2D keypoint estimate. This can be interpreted as sampling from a 2D normal distribution centered on the 2D keypoint estimate, with covariance scaled by the reprojection error.
Next, from the M sampled keypoint sets, the processor 104 obtains M new 6DOF pose estimates. The M 6DOF pose estimates form a distribution of solutions from which the processor 104 calculates a 6DOF standard deviation to represent solution uncertainty. If there is a large variance in statistically plausible 6DOF estimates, then the magnitude of uncertainty should increase accordingly.
In various embodiments, the processor 104 tracks the 6DOF pose of an object over the course of a video using a Kalman filter. The processor 104 updates the Kalman filter with the most recent pose and uses the Kalman filter's resulting mean pose to calculate reprojection error. An extra Kalman filter may be used to smooth uncertainty output.
In various embodiments, the processor 104 produces 3D position of a specific point of interest on the 3D object, after being rotated and translated by the predicted 6DOF pose. The processor 104 tailors uncertainty estimation to the 3D point output. After running the PnP algorithm to obtain a sample pose for each sample keypoint set, the processor 104 uses the sample pose to rotate and translate the 3D object model to calculate a sample 3D point. The result is a distribution over the 3D point of interest. From that distribution, the processor 104 computes a 3D standard deviation to represent solution uncertainty.
In various embodiments, the processor 104 uses λ=3 and M=128 sets of 2D keypoints. Other parameters may be used.
Referring to
In some examples, the block 520 of the method 500 further includes various sub-steps, as shown in
The following is a non-exhaustive list of examples, which may or may not be claimed, of the subject matter, disclosed herein.
The following portion of this paragraph delineates example 1 of the subject matter, disclosed herein. According to example 1, a method includes receiving a 2D image from a camera (which can be a single camera in some examples), determining 2D keypoints of a target object located within the 2D image based on a predefined model of the target object, estimating a 6DOF pose based on the 2D keypoints and a 3D model of the target object, generating an uncertainty value of the 6DOF pose, and outputting the uncertainty value of the 6DOF pose.
The following portion of this paragraph delineates example 2 of the subject matter, disclosed herein. According to example 2, which encompasses example 1, above, determining the 2D keypoints is further based on a trained neural network configured to output keypoint heat maps, wherein pixel intensity values associated with each of the keypoint heat maps indicates a keypoint detection probability . . . .
The following portion of this paragraph delineates example 3 of the subject matter, disclosed herein. According to example 3, which encompasses any of examples 1 or 2, above, the method further comprises computing covariance of each of the keypoint heat maps, computing reprojection errors, scaling the covariance of the heat maps based on the reprojection errors to produce scaled covariance, generating samples of new keypoints based on the scaled covariance, and generating a new 6DOF pose based on the samples of new keypoints. Generating the uncertainty value comprises computing a standard deviation of the new 6DOF pose is a refueling boom.
The following portion of this paragraph delineates example 4 of the subject matter, disclosed herein. According to example 4, which encompasses example 3, above, computing the reprojection errors comprises generating 2D keypoints from the 6DOF pose to produce reprojected 2D keypoints and comparing 2D keypoints from heatmaps to the reprojected 2D keypoints.
The following portion of this paragraph delineates example 5 of the subject matter, disclosed herein. According to example 5, which encompasses any of examples 3 or 4, above, generating the uncertainty value further comprises applying a smoothing algorithm to the standard deviation.
The following portion of this paragraph delineates example 6 of the subject matter, disclosed herein. According to example 6, which encompasses example 5, above, the smoothing algorithm comprises a Kalman filter.
The following portion of this paragraph delineates example 7 of the subject matter, disclosed herein. According to example 7, which encompasses any of examples 1-6, above, outputting the uncertainty value of the 6DOF pose further comprises outputting the uncertainty value of the 6DOF pose to an automated refueling system, a boom operator system, or a pilot director light system.
The following portion of this paragraph delineates example 8 of the subject matter, disclosed herein. According to example 8, a tanker aircraft includes a refueling boom, a camera configured to generate a 2D image of an in-flight refueling operation between a receiver aircraft and the tanker aircraft, a processor, and non-transitory computer readable storage media storing code. The code is executable by the processor to perform operations comprising determining 2D keypoints of a target object located within the 2D image based on a predefined model of the target object, estimating a 6DOF pose based on the 2D keypoints and a 3D model of the target object, generating an uncertainty value of the 6DOF pose, and outputting the uncertainty value of the 6DOF pose.
The following portion of this paragraph delineates example 9 of the subject matter, disclosed herein. According to example 9, which encompasses example 8, above, determining the 2D keypoints is further based on a trained neural network configured to output keypoint heat maps, wherein pixel intensity values associated with each of the keypoint heat maps indicates a keypoint detection probability.
The following portion of this paragraph delineates example 10 of the subject matter, disclosed herein. According to example 10, which encompasses example 9, above, the operations further comprise computing covariance of each of the keypoint heat maps, computing reprojection errors, scaling the covariance of the heat maps based on the reprojection errors to produce scaled covariance, generating samples of new keypoints based on the scaled covariance, and generating a new 6DOF pose based on the samples of new keypoints. Generating the uncertainty value comprises computing a standard deviation of the new 6DOF pose.
The following portion of this paragraph delineates example 11 of the subject matter, disclosed herein. According to example 11, which encompasses example 10, above, computing the reprojection errors comprises generating 2D keypoints from the 6DOF pose to produce reprojected 2D keypoints and comparing 2D keypoints to the reprojected 2D keypoints.
The following portion of this paragraph delineates example 12 of the subject matter, disclosed herein. According to example 12, which encompasses example 13, above, the smoothing algorithm comprises a Kalman filter.
The following portion of this paragraph delineates example 13 of the subject matter, disclosed herein. According to example 13, which encompasses any of examples 10-12, above, the code is executable by the processor to determine one or more geometric relationships between the keypoints projected to 3D space and keypoints on a 3D digital model associated with the receiver aircraft and produce the confidence value based on the one or more geometric relationships.
The following portion of this paragraph delineates example 14 of the subject matter, disclosed herein. According to example 14, which encompasses any of examples 8-13, above, the tanker aircraft further comprises an automated refueling system, a boom operator system, or a pilot director light system. Outputting the uncertainty value and the 6DOF pose further comprises outputting the uncertainty value and the 6DOF pose to the automated refueling system, the boom operator system, or the pilot director light system.
The following portion of this paragraph delineates example 15 of the subject matter, disclosed herein. According to example 15, a system includes a camera configured to generate a 2D image of an in-flight refueling operation between a receiver aircraft and a tanker aircraft, a processor, and non-transitory computer readable storage media storing code. The code is executable by the processor to perform operations comprising determining 2D keypoints of a target object located within the 2D image based on a predefined model of the target object, estimating a 6DOF pose based on the 2D keypoints and a 3D model of the target object, generating an uncertainty value of the 6DOF pose, and outputting the uncertainty value of the 6DOF pose.
The following portion of this paragraph delineates example 16 of the subject matter, disclosed herein. According to example 16, which encompasses example 15, above, determining the 2D keypoints is further based on a trained neural network configured to output keypoint heat maps, wherein pixel intensity values associated with each of the keypoint heat maps indicates a keypoint detection probability.
The following portion of this paragraph delineates example 17 of the subject matter, disclosed herein. According to example 17, which encompasses example 16, above, the operations further comprise computing covariance of each of the keypoint heat maps, computing reprojection errors, scaling the covariance of the heat maps based on the reprojection errors to produce scaled covariance, generating samples of new keypoints based on the scaled covariance, and generating a new 6DOF pose based on the samples of new keypoints. Generating the uncertainty value comprises computing a standard deviation of the new 6DOF pose.
The following portion of this paragraph delineates example 18 of the subject matter, disclosed herein. According to example 18, which encompasses example 17, above, computing the reprojection errors comprises generating 2D keypoints from the 6DOF pose to produce reprojected 2D keypoints and comparing 2D keypoints to the reprojected 2D keypoints.
The following portion of this paragraph delineates example 19 of the subject matter, disclosed herein. According to example 19, which encompasses any of examples 17 or 18, above, generating the uncertainty value further comprises applying a Kalman filter to the standard deviation.
The following portion of this paragraph delineates example 20 of the subject matter, disclosed herein. According to example 20, which encompasses any of examples 15-19, above, outputting the uncertainty value of the 6DOF pose further comprises outputting the uncertainty value of the 6DOF pose to an automated refueling system, a boom operator system, or a pilot director light system.
Those of skill in the art will appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. Some of the embodiments and implementations are described above in terms of functional and/or logical block components (or modules) and various processing steps. However, it should be appreciated that such block components (or modules) may be realized by any number of hardware, software, and/or firmware components configured to perform the specified functions. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention. For example, an embodiment of a system or a component may employ various integrated circuit components, e.g., memory elements, digital signal processing elements, logic elements, look-up tables, or the like, which may carry out a variety of functions under the control of one or more microprocessors or other control devices. In addition, those skilled in the art will appreciate that embodiments described herein are merely exemplary implementations.
The various illustrative logical blocks, modules, and circuits described in connection with the embodiments disclosed herein may be implemented or performed with a general-purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general-purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such that the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC.
Techniques and technologies may be described herein in terms of functional and/or logical block components, and with reference to symbolic representations of operations, processing tasks, and functions that may be performed by various computing components or devices. Such operations, tasks, and functions are sometimes referred to as being computer-executed, computerized, software-implemented, or computer-implemented. In practice, one or more processor devices can carry out the described operations, tasks, and functions by manipulating electrical signals representing data bits at memory locations in the system memory, as well as other processing of signals. The memory locations where data bits are maintained are physical locations that have particular electrical, magnetic, optical, or organic properties corresponding to the data bits. It should be appreciated that the various block components shown in the figures may be realized by any number of hardware, software, and/or firmware components configured to perform the specified functions. For example, an embodiment of a system or a component may employ various integrated circuit components, e.g., memory elements, digital signal processing elements, logic elements, look-up tables, or the like, which may carry out a variety of functions under the control of one or more microprocessors or other control devices.
In the above description, certain terms may be used such as “up,” “down,” “upper,” “lower,” “horizontal,” “vertical,” “left,” “right,” “over,” “under” and the like. These terms are used, where applicable, to provide some clarity of description when dealing with relative relationships. But, these terms are not intended to imply absolute relationships, positions, and/or orientations. For example, with respect to an object, an “upper” surface can become a “lower” surface simply by turning the object over. Nevertheless, it is still the same object. Further, the terms “including,” “comprising,” “having,” and variations thereof mean “including but not limited to” unless expressly specified otherwise. An enumerated listing of items does not imply that any or all of the items are mutually exclusive and/or mutually inclusive, unless expressly specified otherwise. The terms “a,” “an,” and “the” also refer to “one or more” unless expressly specified otherwise. Further, the term “plurality” can be defined as “at least two.” Moreover, unless otherwise noted, as defined herein a plurality of particular features does not necessarily mean every particular feature of an entire set or class of the particular features.
Additionally, instances in this specification where one element is “coupled” to another element can include direct and indirect coupling. Direct coupling can be defined as one element coupled to and in some contact with another element. Indirect coupling can be defined as coupling between two elements not in direct contact with each other, but having one or more additional elements between the coupled elements. Further, as used herein, securing one element to another element can include direct securing and indirect securing. Additionally, as used herein, “adjacent” does not necessarily denote contact. For example, one element can be adjacent another element without being in contact with that element.
As used herein, the phrase “at least one of”, when used with a list of items, means different combinations of one or more of the listed items may be used and only one of the items in the list may be needed. The item may be a particular object, thing, or category. In other words, “at least one of” means any combination of items or number of items may be used from the list, but not all of the items in the list may be required. For example, “at least one of item A, item B, and item C” may mean item A; item A and item B; item B; item A, item B, and item C; or item B and item C. In some cases, “at least one of item A, item B, and item C” may mean, for example, without limitation, two of item A, one of item B, and ten of item C; four of item B and seven of item C; or some other suitable combination.
Unless otherwise indicated, the terms “first,” “second,” etc. are used herein merely as labels, and are not intended to impose ordinal, positional, or hierarchical requirements on the items to which these terms refer. Moreover, reference to, e.g., a “second” item does not require or preclude the existence of, e.g., a “first” or lower-numbered item, and/or, e.g., a “third” or higher-numbered item.
As used herein, a system, apparatus, structure, article, element, component, or hardware “configured to” perform a specified function is indeed capable of performing the specified function without any alteration, rather than merely having potential to perform the specified function after further modification. In other words, the system, apparatus, structure, article, element, component, or hardware “configured to” perform a specified function is specifically selected, created, implemented, utilized, programmed, and/or designed for the purpose of performing the specified function. As used herein, “configured to” denotes existing characteristics of a system, apparatus, structure, article, element, component, or hardware which enable the system, apparatus, structure, article, element, component, or hardware to perform the specified function without further modification. For purposes of this disclosure, a system, apparatus, structure, article, element, component, or hardware described as being “configured to” perform a particular function may additionally or alternatively be described as being “adapted to” and/or as being “operative to” perform that function.
The schematic flow chart diagrams included herein are generally set forth as logical flow chart diagrams. As such, the depicted order and labeled steps are indicative of one example of the presented method. Other steps and methods may be conceived that are equivalent in function, logic, or effect to one or more steps, or portions thereof, of the illustrated method. Additionally, the format and symbols employed are provided to explain the logical steps of the method and are understood not to limit the scope of the method. Although various arrow types and line types may be employed in the flow chart diagrams, they are understood not to limit the scope of the corresponding method. Indeed, some arrows or other connectors may be used to indicate only the logical flow of the method. For instance, an arrow may indicate a waiting or monitoring period of unspecified duration between enumerated steps of the depicted method. Additionally, the order in which a particular method occurs may or may not strictly adhere to the order of the corresponding steps shown.
Those skilled in the art will recognize that at least a portion of the controllers, devices, units, and/or processes described herein can be integrated into a data processing system. Those having skill in the art will recognize that a data processing system generally includes one or more of a system unit housing, a video display device, memory such as volatile or non-volatile memory, processors such as microprocessors or digital signal processors, computational entities such as operating systems, drivers, graphical user interfaces, and applications programs, one or more interaction devices (e.g., a touch pad, a touch screen, an antenna, etc.), and/or control systems including feedback loops and control motors (e.g., feedback for sensing position and/or velocity; control motors for moving and/or adjusting components and/or quantities). A data processing system may be implemented utilizing suitable commercially available components, such as those typically found in data computing/communication and/or network computing/communication systems.
The term controller/processor, as used in the foregoing/following disclosure, may refer to a collection of one or more components that are arranged in a particular manner, or a collection of one or more general-purpose components that may be configured to operate in a particular manner at one or more particular points in time, and/or also configured to operate in one or more further manners at one or more further times. For example, the same hardware, or same portions of hardware, may be configured/reconfigured in sequential/parallel time(s) as a first type of controller (e.g., at a first time), as a second type of controller (e.g., at a second time, which may in some instances coincide with, overlap, or follow a first time), and/or as a third type of controller (e.g., at a third time which may, in some instances, coincide with, overlap, or follow a first time and/or a second time), etc. Reconfigurable and/or controllable components (e.g., general purpose processors, digital signal processors, field programmable gate arrays, etc.) are capable of being configured as a first controller that has a first purpose, then a second controller that has a second purpose and then, a third controller that has a third purpose, and so on. The transition of a reconfigurable and/or controllable component may occur in as little as a few nanoseconds, or may occur over a period of minutes, hours, or days.
In some such examples, at the time the controller is configured to carry out the second purpose, the controller may no longer be capable of carrying out that first purpose until it is reconfigured. A controller may switch between configurations as different components/modules in as little as a few nanoseconds. A controller may reconfigure on-the-fly, e.g., the reconfiguration of a controller from a first controller into a second controller may occur just as the second controller is needed. A controller may reconfigure in stages, e.g., portions of a first controller that are no longer needed may reconfigure into the second controller even before the first controller has finished its operation. Such reconfigurations may occur automatically, or may occur through prompting by an external source, whether that source is another component, an instruction, a signal, a condition, an external stimulus, or similar.
For example, a central processing unit/processor or the like of a controller may, at various times, operate as a component/module for displaying graphics on a screen, a component/module for writing data to a storage medium, a component/module for receiving user input, and a component/module for multiplying two large prime numbers, by configuring its logical gates in accordance with its instructions. Such reconfiguration may be invisible to the naked eye, and in some embodiments may include activation, deactivation, and/or re-routing of various portions of the component, e.g., switches, logic gates, inputs, and/or outputs. Thus, in the examples found in the foregoing/following disclosure, if an example includes or recites multiple components/modules, the example includes the possibility that the same hardware may implement more than one of the recited components/modules, either contemporaneously or at discrete times or timings. The implementation of multiple components/modules, whether using more components/modules, fewer components/modules, or the same number of components/modules as the number of components/modules, is merely an implementation choice and does not generally affect the operation of the components/modules themselves. Accordingly, it should be understood that any recitation of multiple discrete components/modules in this disclosure includes implementations of those components/modules as any number of underlying components/modules, including, but not limited to, a single component/module that reconfigures itself over time to carry out the functions of multiple components/modules, and/or multiple components/modules that similarly reconfigure, and/or special purpose reconfigurable components/modules.
In some instances, one or more components may be referred to herein as “configured to,” “configured by,” “configurable to,” “operable/operative to,” “adapted/adaptable,” “able to,” “conformable/conformed to,” etc. Those skilled in the art will recognize that such terms (for example “configured to”) generally encompass active-state components and/or inactive-state components and/or standby-state components, unless context requires otherwise.
The foregoing detailed description has set forth various embodiments of the devices and/or processes via the use of block diagrams, flowcharts, and/or examples. Insofar as such block diagrams, flowcharts, and/or examples contain one or more functions and/or operations, it will be understood by those within the art that each function and/or operation within such block diagrams, flowcharts, or examples can be implemented, individually and/or collectively, by a wide range of hardware, software (e.g., a high-level computer program serving as a hardware specification), firmware, or virtually any combination thereof, limited to patentable subject matter under 35 U.S.C. 101. In an embodiment, several portions of the subject matter described herein may be implemented via Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs), digital signal processors (DSPs), or other integrated formats. However, those skilled in the art will recognize that some aspects of the embodiments disclosed herein, in whole or in part, can be equivalently implemented in integrated circuits, as one or more computer programs running on one or more computers (e.g., as one or more programs running on one or more computer systems), as one or more programs running on one or more processors (e.g., as one or more programs running on one or more microprocessors), as firmware, or as virtually any combination thereof, limited to patentable subject matter under 35 U.S.C. 101, and that designing the circuitry and/or writing the code for the software (e.g., a high-level computer program serving as a hardware specification) and or firmware would be well within the skill of one of skill in the art in light of this disclosure. In addition, those skilled in the art will appreciate that the mechanisms of the subject matter described herein are capable of being distributed as a program product in a variety of forms, and that an illustrative embodiment of the subject matter described herein applies regardless of the particular type of signal bearing medium used to actually carry out the distribution. Examples of a signal bearing medium include, but are not limited to, the following: a recordable type medium such as a floppy disk, a hard disk drive, a Compact Disc (CD), a Digital Video Disk (DVD), a digital tape, a computer memory, etc.; and a transmission type medium such as a digital and/or an analog communication medium (e.g., a fiber optic cable, a waveguide, a wired communications link, a wireless communication link (e.g., transmitter, receiver, transmission logic, reception logic, etc.), etc.).
With respect to the appended claims, those skilled in the art will appreciate that recited operations therein may generally be performed in any order. Also, although various operational flows are presented in a sequence(s), it should be understood that the various operations may be performed in other orders than those which are illustrated or may be performed concurrently. Examples of such alternate orderings may include overlapping, interleaved, interrupted, reordered, incremental, preparatory, supplemental, simultaneous, reverse, or other variant orderings, unless context dictates otherwise. Furthermore, terms like “responsive to,” “related to,” or other past-tense adjectives are generally not intended to exclude such variants, unless context dictates otherwise. The present subject matter may be embodied in other specific forms without departing from its spirit or essential characteristics. The described examples are to be considered in all respects only as illustrative and not restrictive. All changes which come within the meaning and range of equivalency of the claims are to be embraced within their scope.
